data analysis process

Data analysis process

Data collection and preparation


Collect data

Prepare codebook

Set up structure of data

Enter data

Screen data for errors

Exploration of data

Exploration of data

Descriptive Statistics

Graphs

AnalysisAnalysis

Explore relationship

between variables

Compare groups

Collecting data

Survey Using existing data

PREPARING A DATA FILE

Three steps to

Step 1: Setting your options: Edit>Options

General tab Variable lists Output notification

Raise viewer window Scroll to new output

Output No scientific notation

Session journal Append Browse set folder

Data tab Calculate values

immediately Display format for new

numeric variables (make 0)

Output labels Values and labels

Charts tab Formatting charts

Pivot Tables tab academic

Step 2: setting up the structure of the data fileVariable tab SPSS codebook Label versus name Types of variables Values of a variable Missing values Type of measurement

Step 3: enter the data

Data view Entering data Opening an existing data set

Using Excel Variable names in the first row Converting to SPSS format

Other useful things to know: Merging files

Adding cases Adding variables

Sorting the data file Splitting the data file Selecting cases Using sets (useful for large data files with

scales) Data file information

SCREENING AND CLEANING THE DATA

Two step to

Two steps:

Step 1: checking for errors

Categorical variables Analyze>descriptive

statistics>frequencies Statistics: min/max

Numerical variables analyze>descriptive

statistics>descriptives Options: mean, std dev,

min, max

Step 2: finding and correcting the errors

Method 1: sort cases Method 2: edit>find

Other useful thing for screening

Case summaries1. Analyze>reports>case summaries2. Choose variables, limit cases to first 1003. Statistics: number of cases4. Options: uncheck subheadings for totals




Collect data

Prepare codebook


Enter data


Exploration of data

Exploration of data


Graphs

AnalysisAnalysis


between variables

Compare groups

EXPLORATION OF DATA

Descriptive statistics and graphs

Descriptive statistics

Categorical:Frequencies

Numerical:Descriptives:

mean standard deviation minimum maximum skewness (symmetry) kurtosis (peakness)

Two potential problems with missing data:

1. Large amount of missing data number of valid cases decreases drops the statistical power

2. Nonrandom missing data either related to respondent characteristics and/or to respondent attitudes may create a bias

Missing Data

Missing Data Analysis

Examine missing data

By variable By respondent By analysis

If no problem found, go directly to your analysisIf a problem is found:

Delete the cases with missing dataTry to estimate the value of the missing data

Use Analyze > Descriptive Statistics > Frequencies

Look at the frequency tables to see how much missing

If the amount is more than 5%, there is too much. Need analyze further.

Amount of missing data by variable

1. Use transform>count2. Create NMISS in the target variable3. Pick a set of variables that have more

missing data4. Click on define values5. Click on system- or user-missing6. Click add7. Click continue and then ok8. Use the frequency table to show you

the results of NMISS

Missing data by respondent

Use Analyze>descriptive statistics>crosstabs

Look to see if there is a correlation between NMISS (row) and another variable (column)

Use column percents to view the % of missing for the value of the variable

Missing data patterns

Proceed anyway In SPSS Options:

Exclude case listwise include only cases that have all of the variables

Exclude cases pairwise excludes only if the variables in the analysis is missing

Replace with mean use at your own risk

What to do about the missing data?

Assessing Normality

Skewness and kurtosis Using Explore:

Dependent variable Label cases by id variable Display both Statistics descriptives and outliers Plots descriptive: histogram, normality plots

with test options exclude cases pairwise

1. histogram

2. boxplot

Outliers

Other graphs

Bar charts Scatterplots Line graphs

Recoding Calculating When to create a new variable versus

creating a new one.

Manipulating data




Collect data

Prepare codebook


Enter data


Exploration of data

Exploration of data


Graphs

AnalysisAnalysis


between variables

Compare groups

Analysis Crosstabulation/Chi Square Correlation Regression/Multiple regression Logistic regression Factor analysis

Explore relationships

among variables

Non-parametric statistics T-tests One-way analysis of variance

ANOVA Two-way between groups ANOVA Multivariate analysis of variance

MANOVA

Compare groups

Aim: for categorical data to see the relationship between two or more variables

Procedure: Analyze>Descriptive statistics>Crosstab Statistics: correlation, Chi Square, association Cells: Percentages row or column Cluster bar charts

Crosstabulation

CorrelationAim: find out whether a relationship exists and

determine its magnitude and directionCorrelation coefficients:

Pearson product moment correlation coefficientSpearman rank order correlation coefficient

Assumptions:relationship is linearHomoscedasticity: variability of DV should remain constant at all values of IV

Partial correlation

Aim: to explore the relationship between two variables while statistically controlling for the effect of another variable that may be influencing the relationship

Assumptions:same as correlation

cab

RegressionAim: use after there is a significant correlation to find the

appropriate linear model to predict DV (scale or ordinal) from one or more IV (scale or ordinal)

Assumptions:sample size needs to be large enoughmulticollinearity and singularityoutliersnormalitylinearityhomoscedasticity

Types:standardhierarchicalstepwise DV

IV1IV2

IV3

Logistic regressionAim: create a model to predict DV (categorical 2

or more categories) given one or more IV (categorical or numerical/scale)

Assumptions:sample size large enoughmulticollinearityoutliers

Procedure note:use Binary Logistic for DV of 2 categories (coding 0/1)use Multinomial Logistic for DV for more then 2 categories

Factor analysis

Aim: to find what items (variables) clump together. Usually used to create subscales. Data reduction.

Factor analysis:exploratoryconfirmatory

SPSS -> Principal component analysis

Three steps of factor analysis

1. Assessment of the suitability of the data for factor analysis

2. Factor extraction3. Factor rotation and interpretation

1. Assessment of the suitability

1. Sample size: 10 to 1 ratio2. Strength of the relationship among

variables (items) Test of Sphericity3. Linearity4. Outliers among cases

Step 2. Factor extraction1. Commonly used technique principal

components analysis2. Kaisers criterion: only factors with

eigenvalues of 1.0 or more are retained may give too many factors

3. Scree test: plot of the eigenvalues, retain all the factors above the elbow

4. Parallel analysis: compares the size of the eigenvalues with those obtained from randomly generated data set of the same size

Step 3: factor rotation and interpretation1. Orthogonal rotation

1. uncorrelated2. Easier to interpret3. Varimax

2. Oblique rotation1. Correlated2. Harder to interpret3. Direct Oblimin

Checking the reliability of a scale

Analyze>Scale>Reliability Items Model: Alpha Scale label: name new subscale to be

created Statistics:

descriptives: item, scale, scale if item deleted Inter-item: correlations Summaries: correlations

Analysis Crosstabulation/Chi Square Correlation Regression/Multiple regression Logistic regression Factor analysis

Explore relationships

among variables

Non-parametric statistics T-tests One-way analysis of variance ANOVA Two-way between groups ANOVA Multivariate analysis of variance

MANOVA

Compare groups

Nonparametric testsNon-parametric techniques Parametric techniques

Chi-square test for goodness of fit None

Chi-square test for independence None

Kappa measure of agreement None

Mann-Whitney U Test Independent samples t-test

Wilcoxon Signed Rank Test Paired samples t-test

Kruskal-Wallis Test One-way between groups ANOVA

Friedman Test One-way repeated measures ANOVA

Aim:Testing the differences between the means of two independent samples or groups

Requirements: Only one independent (grouping) variable IV (ex. Gender) Only two levels for that IV (ex. Male or Female) Only one dependent variable (DV - numerical)

Assumptions: Sampling distribution of the difference between the means is normally

distributed Homogeneity of variances Tested by Levenes Test for Equality of

Variances Procedure:

ANALYZE>COMPARE MEANS>INDEPENDENT SAMPLES T-TEST Test variable DV Grouping variable IV DEFINE GROUPS (need to remember your coding of the IV) Can also divide a range by using a cut point

T-test for independent groups

Aim:used in repeated measures or correlated groups designs, each subject is tested twice on the same variable, also matched pairs

Requirements: Looking at two sets of data (ex. pre-test vs. post-test) Two sets of data must be obtained from the same subjects or

from two matched groups of subjects

Assumptions: Sampling distribution of the means is normally distributed Sampling distribution of the difference scores should be

normally distributed

Procedure: ANALYZE>COMPARE MEANS>PAIRED SAMPLES T-TEST

Paired Samples T-test

Aim: looks at the means from several independent groups, extension of the independent sample t-test

Requirements: Only one IV (categorical) More than two levels for that IV Only one DV (numerical)

Assumptions: The populations that the sample are drawn are normally

distributed Homogeneity of variances Observations are all independent of one another

Procedure:ANALYZE>COMPARE MEANS>One-Way ANOVA Dependent List DV Factor IV

One-way Analysis of Variance

Aim: test for main effect and interaction effects on the DV

Requirements: Two IV (categorical variables) Only one DV (continuous variable)

Procedure:ANALYZE>General Linear Model>Univariate Dependent List DV Fixed Factor IVs

Two-way Analysis of Variance

MANOVAAim: extension of ANOVA when there is more

than one DV (should be related)Assumptions:

sample sizenormalityoutlierslinearityhomogeneity of regressionmulticollinearity and singularityhomogeneity of variance-covariance matrices



Collect data

Prepare codebook


Enter data


Exploration of data


Graphs

Analysis


between variables

Compare groups

THANK YOU!

data analysis process

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